"""simple docstring"""



from statistics import mean

import numpy as np



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    0
       # Number of processes finished
       __a       =    0
       # Displays the finished process.
       # If it is 0, the performance is completed if it is 1, before the performance.
       __a       =    [0] * no_of_process
       # List to include calculation results
       __a       =    [0] * no_of_process

       # Sort by arrival time.
       __a       =    [burst_time[i] for i in np.argsort(a				)]
       __a       =    [process_name[i] for i in np.argsort(a				)]
       arrival_time.sort()

       while no_of_process > finished_process_count:
              __a       =    0
              while finished_process[i] == 1:
                     i += 1
              if current_time < arrival_time[i]:
                     __a       =    arrival_time[i]

              __a       =    0
              # Index showing the location of the process being performed
              __a       =    0
              # Saves the current response ratio.
              __a       =    0
              for i in range(0   ,    a				):
                     if finished_process[i] == 0 and arrival_time[i] <= current_time:
                            __a       =    (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                                i
                            ]
                     if response_ratio < temp:
                            __a       =    temp
                            __a       =    i

        # Calculate the turn around time
              __a       =    current_time + burst_time[loc] - arrival_time[loc]
              current_time += burst_time[loc]
              # Indicates that the process has been performed.
              __a       =    1
              # Increase finished_process_count by 1
              finished_process_count += 1

       return turn_around_time



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):
       __a       =    [0] * no_of_process
       for i in range(0   ,    a				):
              __a       =    turn_around_time[i] - burst_time[i]
       return waiting_time


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			5
   SCREAMING_SNAKE_CASE__:List[str]									=			["""A""", """B""", """C""", """D""", """E"""]
   SCREAMING_SNAKE_CASE__:List[Any]									=			[1, 2, 3, 4, 5]
   SCREAMING_SNAKE_CASE__:Tuple									=			[1, 2, 3, 4, 5]

   SCREAMING_SNAKE_CASE__:Tuple									=			calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   SCREAMING_SNAKE_CASE__:str									=			calculate_waiting_time(
       process_name, turn_around_time, burst_time, no_of_process
   )

   print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
   for i in range(0, no_of_process):
      print(
          F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
          F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
      )

   print(F'''average waiting time : {mean(waiting_time):.5f}''')
   print(F'''average turn around time : {mean(turn_around_time):.5f}''')

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
